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Update the README example to the 0.5 optimizer interface #237

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0b7e3be
Update the README example to the 0.5 optimizer interface
benedict-96 Aug 16, 2026
d753344
README: plot the example with Makie instead of Plots.jl
benedict-96 Aug 16, 2026
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18 changes: 12 additions & 6 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -18,11 +18,13 @@ At its core every neural network comprises three components: a neural network ar

Traditionally, physical properties have been encoded into the loss function (PINN approach), but in `GeometricMachineLearning.jl` this is exclusively done through the architectures and the optimizers of the neural network, thus giving theoretical guarantees that these properties are actually preserved.

The optimizer methods themselves (`GradientMethod`, `MomentumMethod`, `Adam`, and the caches, global sections and retractions that go with them) come from [`GeometricOptimizers.jl`](https://github.com/JuliaGNI/GeometricOptimizers.jl) and are re-exported here; `GeometricMachineLearning.jl` supplies the part that is specific to neural networks, i.e. walking the parameter tree and the manifold layers.

Using the package is very straightforward and is very flexible with respect to the device `(CPU, CUDA, Metal, ...)` and the type `(Float16, Float32, Float64, ...)` you want to use. The following is a simple example to learn a SympNet on data coming from a pendulum:
```julia
using GeometricMachineLearning
using CUDA # Metal
using Plots
using CairoMakie

include("scripts/pendulum.jl")

Expand All @@ -41,8 +43,8 @@ backend = CUDABackend()
# initialize the network (i.e. the parameters of the network)
g_nn = NeuralNetwork(gsympnet, backend, type)

# call the optimizer
g_opt = Optimizer(AdamOptimizer(), g_nn)
# call the optimizer: the method comes first, the step size is given separately
g_opt = Optimizer(Adam(type), g_nn; step_size = 1e-3)

const nepochs = 300
const batch_size = 100
Expand All @@ -53,9 +55,13 @@ g_loss_array = g_opt(g_nn, dl, Batch(batch_size), nepochs)
# plot the result
ics = (q=qp_data.q[:,1], p=qp_data.p[:,1])
const steps_to_plot = 200
g_trajectory = Iterate_Sympnet(g_nn, ics; n_points = steps_to_plot)
p2 = plot(qp_data.q'[1:steps_to_plot], qp_data.p'[1:steps_to_plot], label="training data")
plot!(p2, g_trajectory.q', g_trajectory.p', label="G Sympnet")
g_trajectory = iterate(g_nn, ics; n_points = steps_to_plot)
fig = Figure()
ax = Axis(fig[1, 1]; xlabel = L"q", ylabel = L"p")
lines!(ax, qp_data.q[1, 1:steps_to_plot], qp_data.p[1, 1:steps_to_plot], label = "training data")
lines!(ax, g_trajectory.q[1, :], g_trajectory.p[1, :], label = L"$G$-SympNet")
axislegend(ax)
fig
```
More examples like this can be found in the docs.

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